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Micro Targeting: a new ingredient for customer engagement

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Humans are inherently lazy, which may be a trait left over from our ancestors’ days of conserving energy for the next hunt. Our nervous system continuously optimizes movement, in real time, to use the least amount of energy possible; therefore it comes as no surprise that since time immemorial, humans have had the fascination to build things which are less time consuming and more efficient. The idea is to make human effort less redundant and more valuable than what currently exists. This has led to industry led technology disruptions such as automation that helps in accelerating business outcomes that supersedes its potential.

Automation is rapidly shifting the way organizations sustain customer relationships and drive their marketing practices. Now, more than ever, AI has become the new driving force for automation in devising intelligent marketing initiatives. AI enabled automation is used in recommendation engines to increase conversions, dynamic content assortment, and real time changes of display styles leading to personalization of customer experiences. AI empowers marketers to map product information with on-demand customer needs making the advent of this technology contextually relevant in today’s times. Today’s customer is digitally connected and to meet his / her demands, marketers need to be focused on proactive individualized dialog driven customer engagement. This individualization is possible by identifying patterns in complex data sets and automating actions based on the insights generated through AI. These hidden patterns are the key to increased customer retention, advocacy and new sales conversions.

It is because of the initiation and success of AI in the marketing space that it has become a critical component of every successful marketer’s arsenal. The emphasis on hyper-personalization to create individualized experiences is quickly gaining traction. This has led to micro-targeting being increasingly used by brands to target niche audiences with specific content to maximize conversion rates. This reduces communication gaps and builds a sustainable level of trust to foster long term relationships.

In order to use the benefits of micro-targeting, data quality is critical. To enhance data quality there is a need to create a probabilistic model of historical and contextual data that predicts and tracks customer actions to provide actionable insights. This concept is called predictive analytics which leverages your data to foresee marketing outcomes. It is like looking into a crystal ball, except that it is not the fortune teller but your own data that is giving you the confidence in taking marketing decisions with a considerable degree of certainty and accuracy. The potency of this form of analytics is further enhanced by leveraging Big Data, which helps in nurturing qualified leads, understanding customer spending patterns and streamlining campaign costs across channel mix through fact based predictions.

Having understood the power of data, contextualization is the next facet that needs to be factored in for impactful micro-targeting campaigns. Knowing the customer context through situational data and visit histories helps in identifying circumstances having higher possibilities of customer engagement. Here are few areas you can focus on for effective micro-targeting:

Map the customer journey.

Narrow down the target user category. Make it highly relevant and accurate. Have a reliable data set. Precise micro-targeting is possible with exceptional detailing of data to enable finer segregation of user groups.

Showcase the product or service to the target customer and have a micro targeted conversation. Know when to engage with them.

The use of AI driven predictive analytics and Big Data enables profitable decision making and guarantees the success of micro-targeting campaigns. This twin combination can help brands understand deeper relationships between products and campaigns and the impact it has on overall sales conversions. These technologies can also infer a campaign’s after effects by examining large data sets using wide ranging variables, while self-adjusting itself through new data inputs to find optimal outcomes. Through micro-targeting, customers will become more receptive to campaigns, since they will find them more relevant and contextual.

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